A guide to the top AI gateways that provide the essential observability and monitoring dashboards needed to run LLM applications in production. This comparison reviews the leading options, with Bifrost emerging as the top choice for teams that require enterprise-grade performance, security, and comprehensive visibility.
Managing production AI applications without visibility into cost, latency, and error rates is a significant operational risk. An AI gateway acts as a central control plane for all LLM traffic, making it the ideal point to implement robust monitoring. Gateways with built-in observability dashboards provide immediate insights into key performance indicators, helping teams debug issues, control spending, and optimize user experience. Bifrost, a high-performance, open-source AI gateway from Maxim AI, is designed with this principle at its core, offering detailed observability alongside routing, failover, and governance capabilities.
This article compares the nine best AI gateways that feature built-in observability dashboards, evaluating them on the metrics they track, their integration capabilities, and their suitability for different scales of operation.
Key Criteria for Evaluating AI Gateway Observability
Effective observability in an AI gateway is more than just a simple request log. When evaluating options, engineering and platform teams should look for specific capabilities that provide actionable insights. The key criteria include:
- Core Metrics Tracked: The dashboard should display real-time data on requests per minute, latency (p50, p90, p99), token usage (input and output), and error rates by model, provider, and API key.
- Cost Analysis: The ability to translate token usage into actual costs is critical for budget management. The best dashboards provide granular cost breakdowns by user, project, or virtual key.
- Alerting and Anomaly Detection: The system should support configurable alerts for spikes in latency, budget overruns, or unusual error patterns, enabling proactive incident response.
- Data Granularity and Filtering: Operators need to drill down into the data. Look for the ability to filter logs and metrics by time range, model, provider, custom metadata, and user identity.
- Integration with External Tools: While built-in dashboards are valuable, a gateway must also integrate with an organization's existing observability stack. Support for standards like Prometheus and OpenTelemetry (OTLP) is essential for sending metrics and traces to systems like Grafana, Datadog, and Honeycomb.
- User Interface (UI) and Usability: The dashboard must be intuitive, presenting complex information in a clear and easily digestible format. A well-designed UI accelerates debugging and analysis. ## The 9 Best AI Gateways with Observability Features
Based on the criteria above, this is a review of the top AI gateways that provide strong, built-in observability features for production workloads.
1. Bifrost
Bifrost is a high-performance AI gateway written in Go, designed for speed, reliability, and enterprise-grade governance. Its observability features are comprehensive, providing deep insights into every aspect of LLM traffic.
The Bifrost dashboard offers real-time visibility into requests, token counts, and cost, all filterable by virtual key, provider, and model. It provides native support for both Prometheus scraping and push gateway, as well as a first-class OpenTelemetry integration for exporting distributed traces. For enterprise teams, the Datadog connector offers seamless integration with APM and LLM Observability products. This level of visibility, combined with its high performance—benchmarks show just 11 microseconds of overhead at 5,000 RPS—makes it a superior choice.
Furthermore, Bifrost's observability extends beyond the data center. Its governance and security controls can be extended to employee machines with Bifrost Edge, which provides unified visibility into both server-side and endpoint AI application usage.
- Best for: Enterprises and high-growth companies that need a scalable, low-latency, and highly extensible AI gateway with comprehensive, integrated observability from the cloud to the endpoint.
- Key Features: Real-time dashboards, Prometheus and OpenTelemetry exports, Datadog connector, cost tracking per virtual key, and endpoint visibility via Bifrost Edge.
2. LiteLLM
LiteLLM is a popular open-source library for simplifying calls to a wide range of LLM APIs. It includes a UI that provides a dashboard for viewing API usage, spend, and errors. It's a lightweight and flexible option for teams that need a unified API layer with basic observability. Its dashboard is useful for quickly tracking usage across different models and can be self-hosted, offering good control over data.
- Best for: Startups and development teams looking for a simple, open-source way to manage multi-provider LLM access with a straightforward observability UI.
- Key Features: Unified API for 100+ providers, basic usage and cost dashboard, key and virtual key management.
3. Kong AI Gateway
Kong AI Gateway is an extension of the widely adopted Kong API Gateway. It brings enterprise-grade traffic control, security, and observability to AI workloads. Kong provides a full suite of analytics and monitoring capabilities through its own dashboard and integrations. It allows teams to apply policies like rate limiting and caching and observe their impact on performance and cost directly.
- Best for: Large organizations already invested in the Kong ecosystem or those needing a comprehensive API management solution that extends to AI services.
- Key Features: Advanced traffic control, AI-specific plugins, native analytics dashboard, and integration with enterprise monitoring tools.
4. Cloudflare AI Gateway
Cloudflare AI Gateway provides analytics, caching, and logging for AI applications. As part of the Cloudflare ecosystem, it leverages a massive global network to reduce latency. Its dashboard provides insights into requests, token usage, costs, and errors. It is particularly useful for teams that want to manage AI traffic alongside their other web assets on the Cloudflare platform.
- Best for: Teams already using Cloudflare for other services who want a simple, integrated solution for AI observability and caching.
- Key Features: Global low-latency network, analytics dashboard for traffic and costs, request logging, and caching.
5. OpenRouter
OpenRouter is a service that aggregates a wide variety of open-source and proprietary models through a single API, often providing access to models not easily available elsewhere. It offers a simple dashboard that allows users to track their credits (costs), view request history, and monitor usage. While not as feature-rich as dedicated gateways, its observability is sufficient for developers and researchers using its model marketplace.
- Best for: Developers and researchers who need access to a diverse range of models and require basic cost and usage tracking.
- Key Features: Access to hundreds of models, pay-as-you-go pricing, and a simple usage dashboard.
6. Google Cloud Apigee
Apigee, now part of Google Cloud, is a full-lifecycle API management platform. It can be configured to act as an AI gateway, managing and securing traffic to models on Vertex AI or other platforms. Apigee provides powerful analytics and monitoring dashboards that allow organizations to track API performance, developer engagement, and operational metrics, making it a robust choice for enterprise-scale AI deployments.
- Best for: Enterprises on Google Cloud Platform that need a mature API management solution with extensive analytics for their AI services.
- Key Features: Advanced API security, traffic management, and highly customizable analytics dashboards.
7. Azure API Management
For organizations heavily invested in the Microsoft ecosystem, Azure API Management serves as a powerful gateway for Azure OpenAI services. It offers built-in analytics that provide insights into API usage, health, latency, and errors. These metrics can be visualized in the Azure portal or streamed to Azure Monitor for more advanced analysis and alerting.
- Best for: Companies building on the Azure stack that need to govern and monitor access to Azure OpenAI models.
- Key Features: Deep integration with Azure services, built-in analytics and reporting, and enterprise-grade security policies.
8. AWS API Gateway
Amazon API Gateway can be used to create a managed entry point for AI services hosted on AWS, such as Amazon Bedrock or SageMaker endpoints. Observability is primarily handled through integration with Amazon CloudWatch, which provides dashboards for monitoring metrics like API calls, latency, and error rates. While it requires more configuration than some all-in-one solutions, it is a flexible and scalable option for teams on AWS.
- Best for: Organizations building serverless applications on AWS and needing a configurable gateway for their AI/ML endpoints.
- Key Features: Scalable infrastructure, integration with AWS IAM for security, and deep monitoring through CloudWatch.
9. NGINX Plus
NGINX Plus is the commercial version of the popular web server and reverse proxy, and it can be configured as a highly performant and flexible API gateway. It offers a real-time activity monitoring dashboard that gives insights into traffic, performance metrics, and system health. While not AI-specific out of the box, it can be customized with logging and metrics collection to provide deep observability into LLM traffic.
- Best for: Teams with deep NGINX expertise who want to build a custom, high-performance gateway with tailored observability.
- Key Features: High-performance request processing, live activity monitoring dashboard, and extensive custom logging capabilities.
Conclusion and Recommendation
Choosing an AI gateway with strong observability is essential for running reliable and cost-effective AI applications. While many tools offer basic logging, the best options provide dedicated dashboards, cost tracking, and deep integrations with enterprise monitoring platforms.
For teams that need a solution that balances high performance, developer experience, and enterprise-grade features, Bifrost stands out as the best overall choice. Its combination of low-latency routing, granular governance, and comprehensive observability—from its built-in dashboard to its native Prometheus and OpenTelemetry support—provides a complete platform for managing production AI. The addition of endpoint visibility through Bifrost Edge creates a uniquely holistic view of an organization's entire AI footprint.
Teams evaluating AI gateways can request a Bifrost demo or review the open-source repository to learn more.
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